Spatial-Temporal Content Popularity Prediction in Cache Enabled Cellular Networks

Li Li, Hongfeng Tian, Yapeng Wang, Tiankui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

With the development of Internet and mobile communication technology, the mobile network traffic is increasing at exponential rates. Edge caching is a promising technology to reduce network load and content distribution delay. Through content popularity prediction, cache revenue and network per-formance can be improved. This paper proposes a temporal graph convolutional network (TGCN) based content popularity prediction algorithm, which explore the spatial-temporal two-dimensional features in the cellular networks. The proposed TGCN algorithm captures the temporal-dimension dependence from the content request sequence in the base stations (BSs) and the spatial-dimension dependence from different BSs. Then the content request at each BS in the next time cycle is predicted by TGCN. Simulation results show that, compared with the existing algorithms, the proposed algorithm can effectively improve the prediction accuracy of content requests, at least 3%, and improve the cache hit rate of the networks.

Original languageEnglish
Title of host publication2022 21st International Symposium on Communications and Information Technologies, ISCIT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages111-116
Number of pages6
ISBN (Electronic)9781665498517
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event21st International Symposium on Communications and Information Technologies, ISCIT 2022 - Xi'an, China
Duration: 27 Sept 202230 Sept 2022

Publication series

Name2022 21st International Symposium on Communications and Information Technologies, ISCIT 2022

Conference

Conference21st International Symposium on Communications and Information Technologies, ISCIT 2022
Country/TerritoryChina
CityXi'an
Period27/09/2230/09/22

Keywords

  • content popularity prediction
  • edge caching
  • spatial-temporal features

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